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Big Data Analytics: How to Process and Analyze Large Datasets with AI: A Practical Guide to Apache Spark, Hadoop, and Data Engineering
Big Data How to Process and Analyze Large Datasets with AI is a practical guide for data engineers and analysts who want to harness the power of Apache Spark, Hadoop, and AI to process and analyze large datasets. This book provides a step-by-step approach to understanding and implementing big data technologies to gain valuable insights from massive datasets, enabling smarter, data-driven decisions.
You’ll learn how to process data using Apache Spark and Hadoop, and how to integrate them with AI techniques to unlock insights from structured, unstructured, and semi-structured data. With clear explanations and real-world examples, you’ll understand how to scale your data processing workflows and apply machine learning models for advanced analytics.
What You’ll
Introduction to Big Data Get a comprehensive overview of big data technologies like Hadoop and Apache Spark, and understand how they are used to process and analyze large datasets in modern data environments.
Working with Apache Learn how to process data at scale using Apache Spark. Master Spark’s RDDs, DataFrames, and Spark SQL to manipulate, aggregate, and query large datasets efficiently.
AI and Machine Learning in Big Discover how to apply machine learning algorithms in big data environments using Apache Spark’s MLlib and Hadoop’s ecosystem. Learn how to implement predictive models, classification, and clustering algorithms on massive datasets.
Data Pipeline Learn how to build efficient data pipelines for ingesting, processing, and storing data at scale. Explore best practices for designing robust and scalable pipelines using Apache Kafka, NiFi, and Spark.
Real-Time Data Processing with Spark Dive into Spark Streaming to process real-time data in motion, such as streaming logs, financial transactions, or sensor data, and apply machine learning models for real-time analytics.
Handling Structured and Unstructured Understand how to process both structured data (e.g., relational databases) and unstructured data (e.g., logs, social media) using Hadoop and Spark, and learn techniques for data cleansing and transformation.
Optimizing Big Data Learn how to optimize your data processing workflows for performance, including caching, partitioning, and tuning Spark jobs to scale efficiently across large clusters.
Data Visualization and Explore techniques for visualizing big data insights using Apache Zeppelin, Jupyter Notebooks, and other visualization tools to present your findings to stakeholders.
You’ll learn how to process data using Apache Spark and Hadoop, and how to integrate them with AI techniques to unlock insights from structured, unstructured, and semi-structured data. With clear explanations and real-world examples, you’ll understand how to scale your data processing workflows and apply machine learning models for advanced analytics.
What You’ll
Introduction to Big Data Get a comprehensive overview of big data technologies like Hadoop and Apache Spark, and understand how they are used to process and analyze large datasets in modern data environments.
Working with Apache Learn how to process data at scale using Apache Spark. Master Spark’s RDDs, DataFrames, and Spark SQL to manipulate, aggregate, and query large datasets efficiently.
AI and Machine Learning in Big Discover how to apply machine learning algorithms in big data environments using Apache Spark’s MLlib and Hadoop’s ecosystem. Learn how to implement predictive models, classification, and clustering algorithms on massive datasets.
Data Pipeline Learn how to build efficient data pipelines for ingesting, processing, and storing data at scale. Explore best practices for designing robust and scalable pipelines using Apache Kafka, NiFi, and Spark.
Real-Time Data Processing with Spark Dive into Spark Streaming to process real-time data in motion, such as streaming logs, financial transactions, or sensor data, and apply machine learning models for real-time analytics.
Handling Structured and Unstructured Understand how to process both structured data (e.g., relational databases) and unstructured data (e.g., logs, social media) using Hadoop and Spark, and learn techniques for data cleansing and transformation.
Optimizing Big Data Learn how to optimize your data processing workflows for performance, including caching, partitioning, and tuning Spark jobs to scale efficiently across large clusters.
Data Visualization and Explore techniques for visualizing big data insights using Apache Zeppelin, Jupyter Notebooks, and other visualization tools to present your findings to stakeholders.
222 pages, Kindle Edition
Published March 5, 2025
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